{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "cell_id": "ea14541978434e73a78a4f06f7fe013d",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 25
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1693,
    "execution_start": 1660021049397,
    "id": "6D611E573E814A688168A45DC3E02A6D",
    "jupyter": {},
    "scrolled": false,
    "slideshow": {
     "slide_type": "slide"
    },
    "source_hash": "94fbf02b",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集大小： 80\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "dataset = np.loadtxt('kmeans_data.csv', delimiter=',')\n",
    "print('数据集大小：', len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "cell_id": "4c466dd70ee444279fd37a25e8dadd02",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 31
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 872,
    "execution_start": 1660024565094,
    "scrolled": true,
    "source_hash": "afe9f5a6",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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taLE0uXYi2FNq2si1JoO8VJ33EUvNtg4xTAzMPtibbNoIcQLJtSYDjIqlZpur7IO9qaaN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0aedf668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘图函数\n",
    "def show_cluster(dataset, cluster, centroids=None):  \n",
    "    # dataset：数据\n",
    "    # centroids：聚类中心点的坐标\n",
    "    # cluster：每个样本所属聚类\n",
    "    # 不同种类的颜色，用以区分划分的数据的类别\n",
    "    colors = ['blue', 'red', 'green', 'purple']\n",
    "    markers = ['o', '^', 's', 'd']\n",
    "    # 画出所有样例\n",
    "    K = len(np.unique(cluster))\n",
    "    for i in range(K):\n",
    "        plt.scatter(dataset[cluster == i, 0], dataset[cluster == i, 1], color=colors[i], marker=markers[i])\n",
    "\n",
    "    # 画出中心点\n",
    "    if centroids is not None:\n",
    "        plt.scatter(centroids[:, 0], centroids[:, 1], color=colors[:K], marker='+', s=150)  \n",
    "        \n",
    "    plt.show()\n",
    "\n",
    "# 初始时不区分类别\n",
    "show_cluster(dataset, np.zeros(len(dataset), dtype=int))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "cell_id": "0b0f3cf7d7524823b9928f02e0775774",
    "collapsed": true,
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 43
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 7,
    "execution_start": 1660021051460,
    "source_hash": "a7e6ece1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def random_init(dataset, K):\n",
    "    # 随机选取是不重复的\n",
    "    idx = np.random.choice(np.arange(len(dataset)), size=K, replace=False)\n",
    "    return dataset[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "cell_id": "8c289af0217e41818e60030ec62cc2c3",
    "collapsed": true,
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 55
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 3,
    "execution_start": 1660021148342,
    "source_hash": "2709f6b1",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def Kmeans(dataset, K, init_cent):\n",
    "    # dataset：数据集\n",
    "    # K：目标聚类数\n",
    "    # init_cent：初始化中心点的函数\n",
    "    centroids = init_cent(dataset, K)\n",
    "    cluster = np.zeros(len(dataset), dtype=int)\n",
    "    changed = True\n",
    "    # 开始迭代\n",
    "    itr = 0\n",
    "    while changed:\n",
    "        changed = False\n",
    "        loss = 0\n",
    "        for i, data in enumerate(dataset):\n",
    "            # 寻找最近的中心点\n",
    "            dis = np.sum((centroids - data) ** 2, axis=-1)\n",
    "            k = np.argmin(dis)\n",
    "            # 更新当前样本所属的聚类\n",
    "            if cluster[i] != k:\n",
    "                cluster[i] = k\n",
    "                changed = True\n",
    "            # 计算损失函数\n",
    "            loss += np.sum((data - centroids[k]) ** 2)\n",
    "        # 绘图\n",
    "        print(f'Iteration {itr}, Loss {loss:.3f}')\n",
    "        show_cluster(dataset, cluster, centroids)\n",
    "        # 更新中心点\n",
    "        for i in range(K):\n",
    "            centroids[i] = np.mean(dataset[cluster == i], axis=0)\n",
    "        itr += 1\n",
    "\n",
    "    return centroids, cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "cell_id": "8c83828ee3304fce968361dd716506c4",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 67
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 1385,
    "execution_start": 1660024568364,
    "source_hash": "35b5a5ad",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0, Loss 711.336\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0ae3ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, Loss 409.495\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c6a5940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2, Loss 395.264\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c711e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3, Loss 346.068\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c7dc1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 4, Loss 294.244\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c7305f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 5, Loss 178.808\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c644fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6, Loss 151.090\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c679ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "cent, cluster = Kmeans(dataset, 4, random_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "cell_id": "710ba7fb53394c15aa111db21a3f95f6",
    "collapsed": true,
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 79
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 3,
    "execution_start": 1660024355859,
    "source_hash": "76ec3d55",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def kmeanspp_init(dataset, K):\n",
    "    # 随机第一个中心点\n",
    "    idx = np.random.choice(np.arange(len(dataset)))\n",
    "    centroids = dataset[idx][None]\n",
    "    for k in range(1, K):\n",
    "        d = []\n",
    "        # 计算每个点到当前中心点的距离\n",
    "        for data in dataset:\n",
    "            dis = np.sum((centroids - data) ** 2, axis=-1)\n",
    "            # 取最短距离的平方\n",
    "            d.append(np.min(dis) ** 2)\n",
    "        # 归一化\n",
    "        d = np.array(d)\n",
    "        d /= np.sum(d)\n",
    "        # 按概率选取下一个中心点\n",
    "        cent_id = np.random.choice(np.arange(len(dataset)), p=d)\n",
    "        cent = dataset[cent_id]\n",
    "        centroids = np.concatenate([centroids, cent[None]], axis=0)\n",
    "\n",
    "    return centroids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "cell_id": "21cdebcd617f4d9b95bb5da4f88f41e5",
    "deepnote_app_coordinates": {
     "h": 5,
     "w": 12,
     "x": 0,
     "y": 91
    },
    "deepnote_cell_type": "code",
    "deepnote_to_be_reexecuted": false,
    "execution_millis": 899,
    "execution_start": 1660024579498,
    "source_hash": "13182f7c",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0, Loss 373.939\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c25fcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, Loss 158.147\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c7c78d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2, Loss 151.273\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ec014AvrRGnpoDcf0GnroNnRzxqVnRDFcUZQYt8nrGezu/oa5vm5m75J0kaQL3N27Pc7d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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25e0c972dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cent, cluster = Kmeans(dataset, 4, kmeanspp_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1327ee99198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "blobs = make_blobs(random_state=7,centers=3)\n",
    "X_blobs = blobs[0]\n",
    "plt.scatter(X_blobs[:,0],X_blobs[:,1],c='r',edgecolors='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jAZTl8K0dI1qOpErcAFc8cCHOVEfYdkeKgyseCm9NTXptetTFfw2LwU9fLWr0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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13278bd8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(X_blobs)\n",
    "y_kmeans = kmeans.predict(X_blobs)\n",
    "plt.scatter(X_blobs[:, 0], X_blobs[:, 1], c=y_kmeans, s=50)\n",
    "centers = kmeans.cluster_centers_\n",
    "plt.scatter(centers[:, 0], centers[:, 1],marker='x',c='r', s=200, alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1327f51c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "from sklearn.datasets import make_blobs\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "# 生成带有3个中心的随机数据点\n",
    "blobs = make_blobs(random_state=7, centers=3)\n",
    "X_blobs = blobs[0]\n",
    "\n",
    "# 绘制原始数据点\n",
    "plt.scatter(X_blobs[:, 0], X_blobs[:, 1], c='r', edgecolors='k')\n",
    "\n",
    "# 创建KMeans聚类模型，设置聚类数为3\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "kmeans.fit(X_blobs)\n",
    "\n",
    "# 定义图形的边界\n",
    "X_min, X_max = X_blobs[:, 0].min() - 0.5, X_blobs[:, 0].max() + 0.5\n",
    "y_min, y_max = X_blobs[:, 1].min() - 0.5, X_blobs[:, 1].max() + 0.5\n",
    "# 创建网格用于绘制决策边界\n",
    "xx, yy = np.meshgrid(np.arange(X_min, X_max, .02), np.arange(y_min, y_max, .02))\n",
    "\n",
    "# 使用KMeans模型预测网格上的每个点的聚类\n",
    "Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "# 创建新的图形并清空之前的内容\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "# 绘制聚类决策边界\n",
    "plt.imshow(Z, interpolation='nearest', extent=(xx.min(), xx.max(), yy.min(), yy.max()), cmap=plt.cm.summer, aspect='auto', origin='lower')\n",
    "# 绘制原始数据点\n",
    "plt.plot(X_blobs[:, 0], X_blobs[:, 1], 'r.', markersize=5)\n",
    "# 绘制聚类中心\n",
    "centroids = kmeans.cluster_centers_\n",
    "plt.scatter(centroids[:, 0], centroids[:, 1], marker='x', s=150, linewidths=3, color='b', zorder=10)\n",
    "# 设置图形的显示范围\n",
    "plt.xlim(X_min, X_max)\n",
    "plt.ylim(y_min, y_max)\n",
    "# 隐藏坐标轴刻度\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zs3UXoGn2/HWeT8OPLS4gIt8hJJPAd75jNVIlEkwiEQvAIniB9eWX7c+n0yw/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\nusjrXd/W5lmpah7rxsmT0XL6NHaePGlPSImUT1NXV5KpFwCHOqjX5d6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cV0dXY/u+7UoZ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\nhiky/h+9fADrNanesgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1327f32def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "from sklearn.datasets import make_blobs  # 生成随机数据的模块\n",
    "from sklearn.cluster import AgglomerativeClustering  # 导入层次聚类模型\n",
    "import numpy as np  # 数值计算库\n",
    "import matplotlib.pyplot as plt  # 绘图库\n",
    "from itertools import cycle  # 用于循环颜色的迭代器模块\n",
    "\n",
    "# 定义数据中心\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "# 生成随机数据，n_samples是样本数量，centers是中心位置，cluster_std是簇的标准差\n",
    "X, labels_true = make_blobs(n_samples=2000, centers=centers, cluster_std=0.5, random_state=22)\n",
    "# 创建层次聚类模型，使用ward链接方法，期望聚类数为4\n",
    "ac = AgglomerativeClustering(linkage='ward', n_clusters=4)\n",
    "# ac = AgglomerativeClustering(linkage='average', n_clusters=4)\n",
    "# ac = AgglomerativeClustering(linkage='complete', n_clusters=4)\n",
    "# 训练模型并对数据进行聚类\n",
    "ac.fit(X)\n",
    "\n",
    "# 获取每个数据点的分类标签\n",
    "labels = ac.labels_\n",
    "# 绘图\n",
    "plt.figure(1)  # 创建一个新图形\n",
    "plt.clf()  # 清空当前图形\n",
    "# 定义颜色循环，使用'r','g','c','y'四种颜色\n",
    "colors = cycle('rgcy')\n",
    "# 遍历每个聚类标签（0到3），并绘制相应的数据点\n",
    "for k, col in zip(range(4), colors):\n",
    "    # 创建一个布尔数组，标识哪些点属于当前聚类\n",
    "    my_members = labels == k\n",
    "    # 绘制属于当前聚类的点，X[my_members,0]为横坐标，X[my_members,1]为纵坐标\n",
    "    plt.plot(X[my_members, 0], X[my_members, 1], col + '.')\n",
    "# 显示绘制的图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SDbQSQvjBezybJy+LRV2ErH+22jhNRQiBlnkdou2PkPtc3BDqAdzgOQSR/3ad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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1320007e128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets.samples_generator import make_circles\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n",
    "X, y_true = make_circles(n_samples=2000, factor=0.5,noise=0.1)  # 这是环状数据\n",
    "\n",
    "# DBSCAN算法\n",
    "dbscan = DBSCAN(eps=.1, min_samples=10)\n",
    "dbscan.fit(X)  # 该算法对应的两个参数\n",
    "plt.scatter(X[:, 0], X[:, 1], c=dbscan.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V/zDU30NzR+/GsMCe+nf2QN6ZEHiepCYNYwBG3yk5lyoZb87/gRsmTwKBaNye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gAAAG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\nAJ4IzoiwCTQMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13200227d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import DBSCAN\n",
    "X, y_true = make_moons(n_samples=2000, noise=0.1)    # 双半月数据\n",
    "\n",
    "# DBSCAN算法\n",
    "dbscan = DBSCAN(eps=.1, min_samples=10)\n",
    "dbscan.fit(X)  # 该算法对应的两个参数\n",
    "plt.scatter(X[:, 0], X[:, 1], c=dbscan.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B1KrDWnk0umJvtOoMdPk/0OAzYKSLDso1XnCPzLcQUP8wyZ9xGEJPQ929YC0C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\nE2ePH0jf4b2449yHyDugr+hSMpN54KspdDw+thIfXo+X7Kx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cevp92JNsuF2egM2PIiXX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1327f59c748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "X, y_true = make_moons(n_samples=2000,noise=0.1)\n",
    "# KMeans算法\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "kmeans.fit(X)  # 该算法对应的两个参数\n",
    "plt.scatter(X[:, 0], X[:, 1], c=kmeans.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XuVK9Cn6SSONuwRfHUBeA0qlelwIupdO3M7ccQUkpLyX/ewpYR1p7Rup+30kp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1327f54c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 层次聚类\n",
    "ac = AgglomerativeClustering(n_clusters=2)\n",
    "ac.fit(X)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=ac.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "deepnote": {},
  "deepnote_app_layout": "article",
  "deepnote_execution_queue": [],
  "deepnote_notebook_id": "60251fe6bfda4aef99382dc3dd589c38",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
